Cell-type-specific Alzheimer’s disease polygenic risk scores are associated with distinct disease processes in Alzheimer’s disease

Many of the Alzheimer’s disease (AD) risk genes are specifically expressed in microglia and astrocytes, but how and when the genetic risk localizing to these cell types contributes to AD pathophysiology remains unclear. Here, we derive cell-type-specific AD polygenic risk scores (ADPRS) from two extensively characterized datasets and uncover the impact of cell-type-specific genetic risk on AD endophenotypes. In an autopsy dataset spanning all stages of AD (n = 1457), the astrocytic ADPRS affected diffuse and neuritic plaques (amyloid-β), while microglial ADPRS affected neuritic plaques, microglial activation, neurofibrillary tangles (tau), and cognitive decline. In an independent neuroimaging dataset of cognitively unimpaired elderly (n = 2921), astrocytic ADPRS was associated with amyloid-β, and microglial ADPRS was associated with amyloid-β and tau, connecting cell-type-specific genetic risk with AD pathology even before symptom onset. Together, our study provides human genetic evidence implicating multiple glial cell types in AD pathophysiology, starting from the preclinical stage.

The number and proportion of the post-LD shrinkage SNPs (i.e., PRS-CS-processed SNPs) included in each cell-typespecific ADPRS are shown for ROSMAP (N_SNP_ROSMAP) and A4 (N_SNP_A4).Each cell-typespecific ADPRS includes SNPs within cell-type-specific genomic regions (1,343 cell-type-specific genes per each cell type ± 30 kb margins).While the exact numbers of N_SNP_ROSMAP and N_SNP_A4 are slightly different (<5% difference due to genotype missingness in each dataset), the proportions of SNPs included in each cell-type-specific ADPRS were highly consistent.For comparison, total HRC-imputed SNP count before LD shrinkage (N_SNP_HRC) and after LD pruning (N_SNP_PRSet, p-value threshold=1) are also shown for the ROSMAP genotype data.Although LD shrinkage using PRS-CS was limited to the HapMap3 SNPs (N_SNP_ROSMAP and N_SNP_A4), it retains more SNPs with posterior effect sizes than the LD pruning approach (N_SNP_PRSet).3-9), and statistically significant results (FDR<0.025)were indicated in bold.(Also see Fig. 2).3-9), and statistically significant results (FDR<0.025)were indicated in bold.(Also see Fig. 2).  5. Association between cell-type-specific ADPRS and diffuse plaque burden in ROSMAP (n=1,452).Beta (effect size) corresponds to units changed in diffuse plaque burden per 1 s.d.increase in ADPRS.ADPRS models (linear regression) were adjusted for APOE ε4, APOE ε2, age at death, sex, genotyping platform, and the first three genotype principal components.For the cell-typespecific ADPRS that showed a significant association with the trait (FDR<0.025),variance explained by the PRS was captured by comparing adjusted R 2 between the linear models with and without the given PRS term (R 2 ).For comparison, the statistics for APOE ε4 and ε2 from the same model as All-ADPRS (with the same covariates) were shown in the bottom two lines of the table.All p-values are two-sided.To account for multiple comparisons, false discovery rate (FDR) correction was applied across all main tests in ROSMAP (Supplementary Tables 3-9), and statistically significant results (FDR<0.025)were indicated in bold.(Also see Fig. 2).6. Association between cell-type-specific ADPRS and neuritic plaque burden in ROSMAP (n=1,452).Beta (effect size) corresponds to units changed in neuritic plaque burden per 1 s.d.increase in ADPRS.ADPRS models (linear regression) were adjusted for APOE ε4, APOE ε2, age at death, sex, genotyping platform, and the first three genotype principal components.For the cell-typespecific ADPRS that showed a significant association with the trait (FDR<0.025),variance explained by the PRS was captured by comparing adjusted R 2 between the linear models with and without the given PRS term (R 2 ).For comparison, the statistics for APOE ε4 and ε2 from the same model as All-ADPRS (with the same covariates) were shown in the bottom two lines of the table.All p-values are two-sided.To account for multiple comparisons, false discovery rate (FDR) correction was applied across all main tests in ROSMAP (Supplementary Tables 3-9), and statistically significant results (FDR<0.025)were indicated in bold.(Also see Fig. 2).).Beta (effect size) corresponds to units changed in neuritic plaque burden per 1 s.d.increase in ADPRS.ADPRS models (linear regression) were adjusted for APOE ε4, APOE ε2, age at death, sex, genotyping platform, and the first three genotype principal components.For the cell-type-specific ADPRS that showed a significant association with the trait (FDR<0.025),variance explained by the PRS was captured by comparing adjusted R 2 between the linear models with and without the given PRS term (R 2 ).For comparison, the statistics for APOE ε4 and ε2 from the same model as All-ADPRS (with the same covariates) were shown in the bottom two lines of the table.All p-values are twosided.To account for multiple comparisons, false discovery rate (FDR) correction was applied across all main tests in ROSMAP (Supplementary Tables 3-9), and statistically significant results (FDR<0.025)were indicated in bold.(Also see Fig. 2).Supplementary Table 9. Association between cell-type-specific ADPRS and cognitive decline (CogDec) in ROSMAP (n=1,374).Beta (effect size) corresponds to units changed in CogDec per 1 s.d.increase in ADPRS.ADPRS models (linear regression) were adjusted for APOE ε4, APOE ε2, genotyping platform, and the first three genotype principal components.For the cell-type-specific ADPRS that showed a significant association with the trait (FDR<0.025),variance explained by the PRS was captured by comparing adjusted R 2 between the linear models with and without the given PRS term (R 2 ).For comparison, the statistics for APOE ε4 and ε2 from the same model as All-ADPRS (with the same covariates) were shown in the bottom two lines of the table.All p-values are two-sided.To account for multiple comparisons, false discovery rate (FDR) correction was applied across all main tests in ROSMAP (Supplementary Tables 3-9), and statistically significant results (FDR<0.025)were indicated in bold.(Also see Fig. 2).

Model
Supplementary Table 13.Association between cell-type-specific ADPRS using different genomic margins and AD endophenotypes in ROSMAP.For the significant findings from the ROSMAP main analyses (FDR<0.025 in Fig. 2), we performed sensitivity analyses using cell-type-specific ADPRS with different genomic margins (genes ± 10kb or ± 100kb For the significant findings from the ROSMAP main analyses (FDR<0.025 in Fig. 2), we benchmarked our approach against PRSet.For the PRSet-derived ADPRS that showed a nominal association with the trait (uncorrected p<0.05), variance explained by the PRS was calculated by comparing adjusted R 2 between the linear models with and without the given PRS term (R 2 ).R 2 from the main results (using PRS-CS) were shown for comparison.

Table 2 .
AD endophenotypes tested in ROSMAP.The mean and standard deviation (s.d.) of the AD endophenotypes tested for their associations with cell-type-specific ADPRSs in ROSMAP are shown.For AD dementia (binary trait), we indicated the number of cases and the proportion out of the case (AD dementia) + control (cognitively unimpaired, no AD pathology) subset used for the analyses with AD dementia as the outcome (n=786).Abbreviations: N_nonmissing, number of participants with non-missing data; sqrt, square root-transformed values a All autosomal SNPs excluding the APOE region.Supplementary Table3.Association between cell-type-specific ADPRS and AD dementia in ROSMAP (case: n=538, control: n=248).OR (odds ratio) of AD dementia per 1 s.d.increase in ADPRS is shown.ADPRS models (logistic regression) were adjusted for APOE ε4, APOE ε2, age at death, sex, years of education, genotyping platform, and the first three genotype principal components.For the celltype-specific ADPRS that showed a significant association with the trait (FDR<0.025),variance explained by the PRS was captured by comparing Nagelkerke's R 2 between the models with and without the given PRS term (R 2 ).For comparison, statistics for APOE ε4 and ε2 from the same model as All-ADPRS (with the same covariates) were shown in the bottom two lines of the table.All p-values are twosided.To account for multiple comparisons, false discovery rate (FDR) correction was applied across all main tests in ROSMAP (Supplementary Tables

Table 8 . Association between cell-type-specific ADPRS and neurofibrillary tangle (NFT) burden in ROSMAP (n=1,452
Supplementary Table7.Association between cell-type-specific ADPRS and tau in ROSMAP (n=1,451).Beta (effect size) corresponds to units changed in tau per 1 s.d.increase in ADPRS.ADPRS models (linear regression) were adjusted for APOE ε4, APOE ε2, age at death, sex, genotyping platform, and the first three genotype principal components.For the cell-type-specific ADPRS that showed a significant association with the trait (FDR<0.025),varianceexplainedby the PRS was captured by comparing adjusted R 2 between the linear models with and without the given PRS term (R 2 ).For comparison, the statistics for APOE ε4 and ε2 from the same model as All-ADPRS (with the same covariates) were shown in the bottom two lines of the table.All p-values are two-sided.To account for multiple comparisons, false discovery rate (FDR) correction was applied across all main tests in ROSMAP (Supplementary Tables3-9), and statistically significant results (FDR<0.025)wereindicated in bold.(so see Fig.2).

Supplementary Table 11. Association between cell-type-specific ADPRS and tau in ROSMAP (n=1,451), excluding genes overlapping with Mic-ADPRS.
Beta (effect size) corresponds to units changed in tau per 1 s.d.increase in ADPRS.Ex-, Ast-, and Oli-ADPRS were calculated after excluding genes overlapping with Mic-ADPRS.ADPRS models (linear regression) were adjusted for APOE ε4, APOE ε2, age at death, sex, genotyping platform, and the first three genotype principal components.All p-values are two-sided and not adjusted for multiple comparisons.

Supplementary Table 14. Association between cell-type-specific ADPRS and AD endophenotypes in ROSMAP, using 10 genotype PCs.
).All models (logistic regression for AD dem outcome, linear regression for others) were adjusted for APOE ε4, APOE ε2, age, sex, genotyping platform, years of education (only for AD dem and CogDec), and the first three genotype principal components.All p-values are two-sided and not adjusted for multiple comparisons.Abbreviations: AD dem, AD with dementia; CogDec, cognitive decline; OR, odds ratio.For the significant findings from the ROSMAP main analyses (FDR<0.025 in Fig.2), we performed sensitivity analyses adjusting for 10 genotype PCs (instead of 3 PCs adjusted in the main analyses).All models (logistic regression for AD dem outcome, linear regression for others) were adjusted for APOE ε4, APOE ε2, age, sex, genotyping platform, years of education (only for AD dem and CogDec), and the first three genotype principal components.All pvalues are two-sided and not adjusted for multiple comparisons.Abbreviations: AD dem, AD with dementia; CogDec, cognitive decline; OR, odds ratio; PCs, principal components.

Table 17 .
All models (logistic regression for AD dem outcome, linear regression for others) were adjusted for APOE ε4, APOE ε2, age, sex, genotyping platform, years of education (only for AD dem and CogDec), and the first three genotype principal components.All p-values are two-sided and not adjusted for multiple comparisons.Mediation models based on linear regression were run using non-parametric bootstrapping over 10,000 simulations, and 95% bootstrap confidence intervals and empiric two-sided p-values are shown.Also see Fig.3.First three models were adjusted for APOE ε4, ε2, age at death, sex, education, genotyping batch, and first three genotype principal components (PC1-3).The Mic à NFT à CogDec model was adjusted for neuritic plaque (NP) burden, APOE ε4, ε2, genotyping batch, and PC1-3.The slope of cognitive decline (CogDec) was already adjusted for age, sex, and education.Abbreviations: ACME, average causal mediated effects.ADE, average direct effects.CogDec, cognitive decline.DP, diffuse plaque.NFT, neurofibrillary tangle.NP, neuritic plaque.AD endophenotypes tested in A4.The mean and standard deviation (s.d.) of the AD endophenotypes tested for their associations with cell-type-specific ADPRSs in A4 are shown.Abbreviations: N_nonmissing, number of participants with non-missing data.Abbreviations: HV, hippocampal volume; PACC, Preclinical Alzheimer Cognitive Composite; SUVR, standardized uptake value ratio.